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所在平台: Udemy |
课程主页: https://www.udemy.com/course/build-intelligent-multi-agent-applications-with-autogen/
课程评论：没有评论
课程名称：使用 AutoGen 0.5 构建智能多智能体应用 课程概述：人工智能的未来是多智能体协作，智能代理共同高效解决复杂任务。本课程旨在帮助您掌握 AutoGen 框架（版本 0.4 和 0.5），这是一个强大的工具，用于构建和协调相互作用、推理和协作的 AI 代理。无论您是 AI 爱好者、开发人员还是研究人员，该课程将为您提供构建和部署可扩展多智能体应用的技能。 您将学习： - 多智能体系统的基础：理解 AI 代理的核心组件、应用场景及其如何增强 AI 驱动的工作流程。 - 设置开发环境：学习如何安装 Python、设置 VS Code、创建虚拟环境并安装必要依赖。 - 深入了解 AutoGen：了解 AutoGen 的架构、库和功能，包括使用 OpenAI 和开源 LLaMA 模型。 - AutoGen 关键概念：掌握代理消息传递、用户代理、助手代理、流式响应和多模态 AI 集成。 - 基于团队的 AI 代理协作：学习如何将 AI 代理组织为团队、定义终止条件并实现基于选择的 LLM 代理选择功能。 - 高级多智能体概念：探索 AI 工作流中的状态管理，并深入了解 Magentic-One，这是一种用于网络和文件任务的通用多智能体系统。 - 实践项目：实施真实的 AI 代理应用，包括： - 项目 1：开发基于 Streamlit 的 AI 代理应用，理解和优化代码与代理。 - 项目 2：构建一个多智能体 AI 聊天机器人，为电子商务门户的用户提供动态查询处理。 适合人群： - 有志于构建智能 AI 代理的 AI 和 ML 从业者 - 对 AutoGen、AutoGen AgentChat 及 AI 自动化感兴趣的开发人员 - 探索多智能体协作的研究人员 - 渴望开发 AI 驱动应用程序的任何人 完成本课程后，您将获得构建和优化基于 AutoGen 的 AI 驱动多智能体系统的实践经验，从而为开发下一代 AI 解决方案奠定基础。
The future of AI is multi-agent collaboration, where intelligent agents work together to solve complex tasks efficiently. This course is designed to help you master the AutoGen framework (v 0.4 and v 0.5), a powerful tool for building and orchestrating AI agents that interact, reason, and collaborate. Whether you're an AI enthusiast, a developer, or a researcher, this course will equip you with the skills to build and deploy scalable multi-agent applications.What You Will LearnFundamentals of Multi-Agent Systems - Understand the core components of AI agents, their use cases, and how they enhance AI-driven workflows.Setting Up Your Development Environment - Learn how to install Python, set up VS Code, create virtual environments, and install necessary dependencies.Deep Dive into AutoGen - Explore AutoGen's architecture, libraries, and capabilities, including working with OpenAI and open-source LLaMA models.Key AutoGen Concepts - Master agent messaging, user proxy agents, assistant agents, streaming responses, and multi-modal AI integration.Team-Based AI Agent Collaboration - Learn how to organize AI agents into teams, define termination conditions, and implement SelectorGroupChat for LLM-based agent selection.Advanced Multi-Agent Concepts - Explore state management in AI workflows and dive into Magentic-One, a generalist multi-agent system for web and file-based tasks.Hands-On Projects - Implement real-world AI agent applications, including:Project 1: Develop a Streamlit-based AI Agent App, understand and optimize code with agentProject 2: Build a Multi-Agent AI Chatbot for Customer Support that interacts with users of ecommerce portal and processes queries dynamically.Who Should Take This Course?AI & ML practitioners looking to build intelligent AI agentsDevelopers interested in AutoGen, AutoGen AgentChat and AI automationResearchers exploring multi-agent collaborationAnyone eager to develop AI-powered applicationsBy the end of this course, you will have hands-on experience building and optimizing AI-driven multi-agent systems using AutoGen, setting you up to develop next-gen AI solutions.
